AndreaBozzo/ceres-open-data-index huggingface dataset:
Pathogenic Leptospiral data and ANTILOPE rainfall data on the La Foa Pierrat watershed and gauging station (New Caledonia, SW Pacific), 2020-2024.

[Lien vers la fiche source] The SPIraL project, funded by the French National Research Agency (ANR) was partly devoted to the relationships between hydrological variables and pathogenic Leptospira concentrations in the La Foa River. Pathogenic Leptospira concentrations were measured at the La Foa Pierrat gauging station according to the method exposed in Thibeaux et al., STOTEN, 2024, [ doi: 10.1016/j.scitotenv.2023.168700 ] ANTILOPE composite data are rainfall data computed from the radar installed at Noumea and raingauge data measured in the Meteo France network of New Caledonia. These data are provided with a 1 km2 spatial resolution and at a 15-minute time step. Rainfall data provided here are averaged on the La Foa Pierrat watershed, using the “volume” routine of the Grass Gis software and linearly interpolated at a 6 minute time step. Le projet [SPIraL], financé par l'Agence Nationale de la Recherche Française (ANR) s'est intéressé pour partie à la relation entre les variables hydrologiques et la concentration en Leptospires pathogènes dans la rivière La Foa (Nouvelle Calédonie). Les concentrations en Leptospires ont été mesurées à la station La Foa Pierrat suivant la méthode exposée dans Thibeaux et al. STOTEN, 2024, [ doi: 10.1016/j.scitotenv.2023.168700 ] Les données composites ANTILOPE sont des données de pluie calculées à partir du radar déployé à Nouméa et des données du jeu de pluviomètres de Météo France en Nouvelle Calédonie. Ces données sont fournies à une résolution de 1 km2 et à un pas en temps de 15 minutes. Elles ont été moyennées sur l'ensemble du bassin versant de la Foa-Pierrat en utilisant la fonction “volume” du logiciel Grass-Gis et interpolées linéairement à un pas en temps de 6 minutes.
Contours des bassins versants et réseau hydrographique de l'El Beid au Cameroun

[Lien vers la fiche source] Jeux de données SIG et débits mensuels par bassins versants d'Afrique de l'Ouest et Centrale. Les fichiers fournis sont des fichiers d'entrée des modèles (couches vecteur SIG au format shapefile) des bassins versants constituées à partir des données cartographiques des 356 bassins référencés sous la base SIEREM. [ http://www.hydrosciences.fr/sierem/produits/produits/livret.pdf] La principale station de mesure hydrométrique du bassin versant de l'El Beid est : * rivière El Beid à Fotokol Gambarou Tous les fichiers de données sont au format ArcGIS (shapefiles) : * les contours des bassins versants sont représentés par des polygones (dossier « SIEREM_Elbeid_Cameroon_Rivers_Basin ») * le réseau hydrographique du bassin versant est représenté linéairement (dossier « Sierem_ElBeid_Cameroon_Rivers ») Le fichier au format mxd contient les propriétés d'affichages des informations géographiques gérées dans la carte (symbologie...). Le fichier de métadonnées au format xml créé avec le logiciel ArcCatalog répond à la norme ISO 19115 relative aux métadonnées géographiques. Il contient les références à citer pour chaque type de données disponibles, la signification de la table attributaire, le rectangle d'emprise de la carte etc. Pour chaque station une chronique de débits mensuels issus du système d'informations SIEREM complète le jeu de données (format texte)
Causal network dataset derived from participatory modelling of territorial dynamics in the six intervention areas of the PACTE project

[Lien vers la fiche source] For the 26 territorial units composing the six intervention areas of the PACTE project, models of territorial dynamics were built with local actors elucidating, in the form of networks, cause-and-effect relationships between the policy and institutional contexts, key environmental and socioeconomic dynamics and local livelihood strategies. This dataset is composed of tabular data describing the nodes and edges of the resulting causal network.
60 ans de pluies mensuelles en Afrique - Période 1940-1999

[Lien vers la fiche source] 60 ans de pluies mensuelles en Afrique - Période 1940-1999 HydroSciences Montpellier a hérité du laboratoire d'hydrologie de l'IRD, d'une très importante base de données hydroclimatiques sur l'Afrique de l'Ouest et Centrale, nommée SIEREM. Différents programmes de recherche ont permis aux hydrologues d'HSM d'enrichir cette base sur la période post 1980 et sur la totalité du continent africain. Un long travail de critique a permis de constituer une base de données de référence des pluies d'Afrique au pas de temps mensuel. En raison de données trop rares et trop hétérogènes sur le début du XXème siècle, les grilles ont été calculées de 1940 à 1999 (720 mois). Nous proposons en téléchargement les grilles (un fichier par décennie, 120 grilles par fichier) calculées à partir des données observées aux stations. La méthode est l'Inverse Distance Pondérée, le pas de temps est mensuel et l'échelle spatiale est le demi-degré carré. Un fichier .pdf à l'intérieur de chaque fichier .zip détaille le mode opératoire de la confection des grilles et la description du fichier. La carte des pluies est disponible au Service Editions IRD.
Arabic and English Spatial Entity Dataset for Animal Disease Surveillance Extracted with PADI-web

[Lien vers la fiche source] As part of the “Arabic Corpus and Entities Dealing with Animal Disease Surveillance Extracted with PADI-web” dataset ([https://doi.org/10.18167/DVN1/2B4WLR]), we built a new dataset containing 284 spatial entities in Arabic, their translation into English (manually validated) and their automatic translation by three automatic tools (DeepL, Microsoft Azure, and Reverso). The dataset was updated with two new columns on September 3, 2025: GeoNames ID and GeoNames Feature Class, enabling the matching of spatial entities to the GeoNames gazetteer. The dataset is organised as a table with twelve columns : * ID: The unique identifier of each article (from PADI-web database) * Arabic Location: The spatial entities in Arabic, manually extracted from 53 articles collected via PADI-web * English Location: The manual translation of spatial entities into English, based on existing field sources such as Google Maps and the GeoNames database * GeoNames ID: The unique ID from the GeoNames database (2022 version of GeoNames: https://www.geonames.org/) corresponding to each spatial entity (empty if no match in GeoNames) * GeoNames Feature Class : The feature class corresponding to the GeoNames ID (empty if no match in GeoNames) * Type: A manually assigned type of spatial entity (country, city, region, village, etc.). * Category: The classification of spatial entities into two categories: absolute spatial entities (ASE) and relative spatial entities (RSE). * Arabic Phrases: The sentence, in Arabic, from which the spatial entity was extracted. * Translation DeepL: The translation of the location by DeepL. * Translation Microsoft Azure: The translation of the location by Microsoft Azure. * Translation Reverso: The translation of the location by Reverso. * English Sentences Translated by DeepL: The translation of the sentence by DeepL. * English Sentences Translated by Microsoft Azure: The translation of the sentence by Microsoft Azure. * English Sentences Translated by Reverso: The translation of the sentence by Reverso. Absolute spatial entities are direct references to precise, locatable geographic spaces, i.e. entities that can be located on a map or in a geographic database (e.g. cities such as Safi, countries such as Morocco, Egypt, etc.). Relative spatial entities are entities defined in relation to at least one other spatial entity, using spatial indicators of a topological nature (for example, “الطود شرق” (El-Tod East), “ناحية تلات” (Talat district), etc.).
Database for "Two decades of tropical soil analyses for a better understanding of soil organic carbon"

[Lien vers la fiche source] The dataset contains two csv files (data.csv and soilproperties.csv) for tabular data with comma separating values, one pdf file (dictionary.pdf) and one the python code (computed_data.py). * data.csv file provides 41 variables according to seven soil properties soilproperties.csv. * dictionary.pdf file describes the 7 soil properties and the 41 variables for each soil sample.
Data used for manuscript “Incorporation of rice husk biochars into the soil for fall armyworm control in maize: potential and processes”

[Lien vers la fiche source] Report of pot experiments conducted in greenhouses in randomized complete block designs with 6 replications and respectively 5 and 6 treatments (including plain soil control), at two locations (Antananarivo, Madagascar & St-Pierre, Réunion)/years (2023 & 2025) with two maize crop cycles at each location/year, with artificial infestation (AI) of maize leaf disks in the laboratory with neonate fall armyworm (FAW) larvae. Observations on maize plants include above-ground biomass, chemical analysis of silicon and nitrogen content, NIRS measurement of leaf electrochemical parameters (pH, redox potential, and electrical conductivity), and per cent leaf disk damage by FAW after 72h of AI. Observations on FAW larvae after 72h of infestation include head capsule widths and individual larvae weights.
OMERE Observatory - A long-term dataset on hydrology and suspended sediments in the Kamech catchment (1994-2023).

[Lien vers la fiche source] This dataset contains Rainfall, Evaporation, Discharge and Suspendend Sediment Concentration data over the period 1994-2023 from Kamech catchment, [OMERE Observatory] (Molenat et al., 2018, [https://dx.doi.org/10.2136/vzj2018.04.0086]). The OMERE long-term observation strategy intends to support integrative multidisciplinary research for elucidating the conditions that improve soil and water management and delivery of ecosystem services in a Mediterranean rainfed cultivated context. This dataset relates directly to the following OMERE scientific sub-objectives: (i) to better understand water flows, erosion, and their natural and anthropogenic factors in the short and long term; (ii) to analyze the cumulative effects of agriculture and land management on mass flows at different scales, from the plot to the watershed or landscape. Please refer to Hamdi et al. (submitted) when using the data: Hamdi et al (2025). A long-term dataset on hydrology and suspended sediments in the Kamech catchment from the OMERE Observatory. Submitted to Earth Surface Science Data
Assessing the impact of agroforestry trees species on microclimate, fiber quality, maize and cotton productivity in the north Cameroon

[Lien vers la fiche source] This dataset describes how different tree species (monospecific vs. mixed) influence microclimate regulation, soil properties, crop productivity, and fiber quality. It includes microclimatic, agronomic, and fiber quality data collected under and outside the tree canopy across different parkland models. This dataset is part of the [DeSIRA INNOVACC](https://capacity4dev.europa.eu/info/innovacc-cameroun_en) Project.
LULCC-KnowText - annotated text segments for knowledge extraction on Land Use and Land Cover change

[Lien vers la fiche source] This dataset contains a corpus of annotated text segments (sentences) extracted from scientific articles on Land Use and Land Cover change (LULCC) in West Africa. Each segment has been evaluated for relevance and categorized according to themes such as LULC, LULCC, DRIVERS, PROCESS, and PRACTICES, supporting automatic knowledge extraction and machine learning applications. The dataset consists of 8 files: * Annotated sentences, divided into 4 tabular files: * annotated_sentences_final_corpus.csv: the whole annotated corpus * annotated_sentences_three_annot_corpus.csv: a subset of sentences annotated by three annotators * annotated_sentences_two_annot_corpus.csv: a subset of sentences annotated by two annotators * annotated_sentences_single_annot_corpus.csv: a subset of sentences annotated by one annotator The sets annotated by three annotators and two annotators were used to evaluate inter-annotator agreement during the refining of annotation guidelines. * datasets_columns_description.pdf: This file contains the description of column names used in each dataset. * metadata_articles.pdf: This file contains the metadata of the scientific articles from which the sentences were extracted for annotation. * sentences_annotation_guidelines.pdf: The annotation guidelines used to manually annotate the sentences.
Wild plants coverage at the borders of six rice fields in western Burkina Faso (2017)

[Lien vers la fiche source] During a project focused on rice microbial biodiversity (root microbiome and pathogens), we performed a pluriannual and multi-site survey (see [https://doi.org/10.23708/8FDWIE]). Because microbial species may be shared between rice and surrounding wild plants, we chose to perform a small survey in 2017 in 6 rice fields. This dataset describes obtained data. It corresponds to a list of wild plant species identified in the borders of six rice fields (from three geographical zones in western Burkina Faso) during a survey performed in 2017. More specifically, the observations and sampling were performed during one day in each geographical zone: 26/10/2017 (Bama: 17BM02 and 17BL02), 28/10/2017 (Karfiguela: 17KA01 and 17KA01), 01/11/2017 (Banzon: 17BZ11 and 17SZ07). The precise location of each of these six fields appears in [https://doi.org/10.23708/8FDWIE] (file: Data_MP_Field_Location_dataset.tab). Three fields correspond to irrigated production system (BM02, BZ11, KA01) while the three others (BL02, SZ07, TG01) are in rainfed lowlands. The observations were performed on two consecutive borders of each rice fields. For each field and each wild plant species, the covering area was estimated: 0: absent; 0.5: rare; 1: covering area < 10%; 2: covering area between 10% and 20%; 3: covering area between 20% and 30%. This information corresponds to the columns 3 to 8 in the dataset. In addition, samples from the five more frequent plant species have been collected for each field for further analyses. This led to a set of 30 samples, and a total of 26 different species. The wild plant species sampled are annotated with "X" in the columns 9 to 14 in the dataset.
Yellow mottle disease epidemiology: RYMV specific detection in field-collected leaves from western Burkina Faso (2015-2019)

[Lien vers la fiche source] This project aims at a better understanding of yellow mottle disease epidemiology, both at the regional level, by comparing irrigated areas to rainfed lowland rice, and at the within-field level. The study area is located in western Burkina Faso, in a 100x100kms region within the three provinces Houet, Kénédougou and Comoé, in the sudanian bioclimatic area. Major study sites correspond to three geographical zones (Bama, Banzon and Karfiguela), each of these zones presenting both an irrigated area and neighboring rainfed lowland. Irrigated areas and rainfed lowland were chosen as “neighboring” to assume similar climatic conditions. We followed up these six sites from 2016 to 2019. Prior to that, an initial survey was performed in 2015 in two areas, all irrigated: Banzon and Karfiguela. Collected rice leaves were tested for the specific serological detection of Rice Yellow Mottle Virus (RYMV) by specific DAS-ELISA. This dataset provides information about each analyzed sample (Data_RYMV_BF_Samples_Dataset.tab), and prevalence (=incidence) data in each studied field (Data_RYMV_BF_Fields_Dataset.tab).
Collection of rice-root associated bacteria sampled in Burkina Faso, 2017-2019

[Lien vers la fiche source] In Burkina Faso, studies have been initiated on the diversity of rice rhizosphere and root microbiomes using metabarcoding (Barro et al., 2022) and culturable bacteria (Sondo et al., 2023) approaches to analyse the effects of rice production systems (irrigated cropping areas vs rainfed lowland conditions) on microbiome diversity. As a continuity of these studies we aimed to develop a large collection of culturable rice-associated bacteria from different rice fields in Burkina Faso to serve as a valuable genetic resource for screening biostimulant and biocontrol strains. In this regard, root samples were collected in rice fields over three consecutive years (2017 to 2019) at 22 sites in two studied rice growing regions in western Burkina Faso (Barro et al., 2021; Barro et al., 2022), and at different rice plant development stages. Phenotypically distinct bacterial colonies obtained from whole roots (total microflora) or from surface sterilised roots (putative endophytes) and subsequently plated on five different culture media (TSA of 10 and 50%, NGN, FNb, Baz, PCAT) were reisolated for further taxonomic and phenotypic characterization. Over 3,800 rice root-associated bacteria were isolated from five bacterial culture media. A working collection of 1,013 isolates was characterized at the taxonomic (by 16S fragment sequencing) and phenotypic (in vitro PGP capacity) levels. The results obtained for 1013 isolates are presented in the table as metadata concerning isolate identity, sampling location, sampling date, 16S rDNA accession numbers and sequences, in vitro PGP capacities.
A modified Gale-Shapley deferred acceptance method for many-to-many supplier-receiver matchings with transfer of divisible goods

[Lien vers la fiche source] We propose a practical solution to the many-to-many matching problem with transfer of divisible goods. The general model considers a set S of suppliers and a set R of receivers. Each supplier has a “stock” and can supply several receivers. Each receiver has “needs” that can be fulfilled by obtaining goods from one or more suppliers. Each receiver (supplier) has a preference list of individual suppliers (receivers) with no ties. The method uses the Gale-Shapley Deferred Acceptance (DA) algorithm in which we introduce the deferred updating of remaining stocks and needs during the transactions. This ensures that the quantities proposed or requested remain consistent between successive turns, thus avoiding a sub-optimal division of goods among several supplier-receiver pairs. The system is represented by a bipartite graph G = (S, R, E). The suppliers of set S and the receivers of set R are the vertices of the graph, and E is the set of edges. Note that the graph is not necessarily complete: a supplier (receiver) may not be connected to all receivers (suppliers). The edges hold "costs" that are values used by the suppliers or the receivers to rank the counterparts they are connected to in order of preference. As in the original GS method, the matching results from an iteration during which one side proposes and the other side rejects or defers its acceptance while updating its decision with better proposals in subsequent turns. The rejected sides make proposals to others down their list of preferences, and the turns continue until the system is stabilized. Unlike for the original GS method, the matching results obtained are identical irrespective of whether the first turn is taken by suppliers or receivers. We illustrate the application of the method using a small dataset composed of 7 suppliers and 15 receivers. In real-world cases the method has successfully been deployed with several thousands of suppliers and receivers, requiring computing time of the order of minutes. The method presented was originally developed for studying biomass exchanges within an insular territory in its transition towards a more circular economy. The motivation is that a more efficient use of agricultural by-products can help reduce the island’s dependency on imported materials, such as chemical fertilisers for crops and concentrates for animal feed.
Contours des bassins versants et réseau hydrographique de la Sanaga au Cameroun

[Lien vers la fiche source] Jeux de données SIG et débits mensuels par bassins versants d'Afrique de l'Ouest et Centrale. Les fichiers fournis sont des fichiers d'entrée des modèles (couches vecteur SIG au format shapefile) des bassins versants constituées à partir des données cartographiques des 356 bassins référencés sous la base SIEREM. [ http://www.hydrosciences.fr/sierem/produits/produits/livret.pdf] Les principales stations de mesures hydrométriques et leur période de débits mensuels observés du bassin versant de la Sanaga sont : Mbam au Bac de Goura period 1951-1983 Noun à BafoussamII period 1952-1980 Sanaga à Edéa period 1944-1983 Sanaga à Goyoum period 1955-1983 Sanaga à Lahore period 1951-1980 Sanaga à Nachtigall period 1951-1983 Sanaga à Nanga Eboko period 1949-1980 Mapé au Pont de Magba (amont) period 1952-1983 Sanaga à SakbayemeS (Songmbengue) period 1956-1977 Tous les fichiers de données sont au format ArcGIS (shapefiles) : * les contours des bassins versants sont représentés par des polygones (dossier « SIEREM_Sanaga_Cameroon_Rivers_Basin ») * le réseau hydrographique du bassin versant est représenté linéairement (dossier « Sierem_Sanaga_Cameroon_Rivers ») Le fichier au format mxd contient les propriétés d'affichages des informations géographiques gérées dans la carte (symbologie...). Le fichier de métadonnées au format xml créé avec le logiciel ArcCatalog répond à la norme ISO 19115 relative aux métadonnées géographiques. Il contient les références à citer pour chaque type de données disponibles, la signification de la table attributaire, le rectangle d'emprise de la carte etc. Pour chaque station une chronique de débits mensuels issus du système d'informations SIEREM complète le jeu de données (format texte)
Contours des bassins versants et réseau hydrographique de l'Ouémé au Bénin

[Lien vers la fiche source] Jeux de données SIG et débits mensuels par bassins versants d'Afrique de l'Ouest et Centrale. Les fichiers fournis sont des fichiers d'entrée des modèles (couches vecteur SIG au format shapefile) des bassins versants constituées à partir des données cartographiques des 356 bassins référencés sous la base SIEREM. [ http://www.hydrosciences.fr/sierem/produits/produits/livret.pdf] Les principales stations de mesures hydrométriques et leur période de débits mensuels observés du bassin versant de l'Ouémé au Bénin sont : Ouémé at Bonou period 1948-1996 code 1114500107 Ouémé at Domé period 1952-1991 code 1114501005 Ouémé at Hetin Sota period 1948-1986 code 1114500113 Ouémé at Kaboua period 1951-1992 code 1114501103 Ouémé at Pont de Bétérou period 1952-1998 code 1114500105 Ouémé at Pont de Savé period 1951-1998 code 1114500119 Ouémé at Sagon period 1951-1987 code 1114500117 Ouémé at Savalou Déversoir period 1952-1985 code 1114503303 Ouémé at So Awa period 1951-1980 code 1114501206 Ouémé at Vossa period 1953-1986 code 1114501302 Tous les fichiers de données sont au format ArcGIS (shapefiles) : * les contours des bassins versants sont représentés par des polygones, * le réseau hydrographique du bassin versant est représenté linéairement Pour chaque station une chronique de débits mensuels issus du système d'informations SIEREM complète le jeu de données (format texte)